Information Package / Course Catalogue
| Civil Engineering | |||||
| Bachelor | Length of the Programme: 4 | Number of Credits: 240 | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF: Level 6 |
ECTS Course Information Package
| School/Faculty/Institute | Faculty of Engineering | ||||||||
| Course Code | COMP 482 | ||||||||
| Course Title in English | Computer Vision | ||||||||
| Course Title in Turkish | Bilgisayarla Görü | ||||||||
| Language of Instruction | EN | ||||||||
| Type of Course | Ters-yüz öğrenme | ||||||||
| Level of Course | Başlangıç | ||||||||
| Semester | Fall | ||||||||
| Contact Hours per Week |
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| Estimated Student Workload | 161 hours per semester | ||||||||
| Number of Credits | 6 ECTS | ||||||||
| Grading Mode | Standard Letter Grade | ||||||||
| Pre-requisites |
COMP 106 - Object-Oriented Programming | COMP 110 - Object-Oriented Programming (JAVA) |
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| Co-requisites | None | ||||||||
| Expected Prior Knowledge | Object Oriented Programming, Data Structures | ||||||||
| Registration Restrictions | Only Undergraduate Students | ||||||||
| Overall Educational Objective | To become familiar with the fundamental concepts of Computer Vision, such as image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, image understanding and object recognition. | ||||||||
| Course Description | This course provides a comprehensive introduction to some fundamental aspects of Computer Vision. The following topics are covered: Introduction, Image formation, camera parameters, preprocessing, convolution, segmentation, edge and corner detection, line and ellipse fitting, Object Tracking, image understanding and object recognition, Deep Learning. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) understand image formation process, camera parameters and projections; 2) apply convolution for filtering and preprocessing; 3) apply probability and statistics to solve problems in computer vision; 4) develop feature extractors such as edge, corner, line extractors; 5) develop solutions using image stitching and stereo images; 6) develop image understanding and objects recognition solutions; 7) communicate effectively by means of reports and presentations; 8) analyze and interpret data, and use engineering judgment to draw conclusions; 9) acquire and apply new knowledge as needed; |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| 1) An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | |||||||||
| 2) An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | |||||||||
| 3) An ability to communicate effectively with a range of audiences | |||||||||
| 4) An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | |||||||||
| 5) An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | |||||||||
| 6) An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | |||||||||
| 7) An ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Relation to Program Outcomes and Competences
| N None | S Supportive | H Highly Related |
| Program Outcomes and Competences | Level | Assessed by | |
| 1) | An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics | H | Exam,Homework,Proje |
| 2) | An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors | H | Exam,Homework,Proje |
| 3) | An ability to communicate effectively with a range of audiences | S | Proje |
| 4) | An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts | N | |
| 5) | An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives | N | |
| 6) | An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions | S | Exam,Homework,Proje |
| 7) | An ability to acquire and apply new knowledge as needed, using appropriate learning strategies | S | Exam,Homework,Proje |
| Prepared by and Date | MUHİTTİN GÖKMEN , April 2018 |
| Course Coordinator | MUHİTTİN GÖKMEN |
| Semester | Fall |
| Name of Instructor | Prof. Dr. MUHİTTİN GÖKMEN |
Course Contents
| Week | Subject |
| 1) | Introduction |
| 2) | Image formation |
| 3) | Camera parameters |
| 4) | Preprocessing: Histogram modifications |
| 5) | Convolution and noise reduction |
| 6) | Edge and corner detection |
| 7) | Line, circle and ellipse fitting |
| 8) | RANSAC and Homography |
| 9) | Binocular Stereo |
| 10) | Image Understanding |
| 11) | Object Recognition-PCA |
| 12) | Object Recognition-Neural Networks |
| 13) | Object Recognition-Deep Learning |
| 14) | Project presentations |
| 15) | Final Exam/Project/Presentation Period |
| 16) | Final Exam/Project/Presentation Period |
| Required/Recommended Readings | Computer Vision: Algorithms and Applications, Richard Szeliski, Springer Science & Business Media, 2010 Introductory Techniques for 3-D Computer Vision, by Emanuele Trucco, Alessandro Verri, Prentice-Hall, 1998 | |||||||||||||||
| Teaching Methods | Lecturing and exercises in the classroom with computers. In-class exercises and 2 Projects will be carried out by students | |||||||||||||||
| Homework and Projects | In-class exercises, 2 Projects, 2 Midterm exams, Term project | |||||||||||||||
| Laboratory Work | ||||||||||||||||
| Computer Use | For program development to solve computer vision problems. | |||||||||||||||
| Other Activities | None | |||||||||||||||
| Assessment Methods |
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| Course Administration |
gokmenm@mef.edu.tr Instructor’s office and phone number, office hours, email address: To be announced -Office: 5th Floor, #18 -Phone number: 0 212 395 36 26 - Email address: gokmenm@mef.edu.tr Rules for attendance: Minimum of 70% attendance required. Missing a quiz: Provided that proper documents of excuse are presented, each missed quiz by the student will be given a grade which is equal to the average of all of the other quizzes. No make-up will be given. Missing a midterm: Provided that proper documents of excuse are presented, each missed midterm by the student will be given the grade of the final exam. No make-up will be given. Missing a final: Faculty regulations. A reminder of proper classroom behavior, code of student conduct: YÖK Regulations Statement on plagiarism: YÖK Regulations http://3fcampus.mef.edu.tr/uploads/cms/webadmin.mef.edu.tr/4833_2.pdf |
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ECTS Student Workload Estimation
| Activity | No/Weeks | Hours | Calculation | ||||
| No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
| Course Hours | 14 | 1 | 3 | 1 | 70 | ||
| Project | 1 | 35 | 7 | 42 | |||
| Quiz(zes) | 3 | 4 | 1 | 15 | |||
| Midterm(s) | 2 | 15 | 2 | 34 | |||
| Total Workload | 161 | ||||||
| Total Workload/25 | 6.4 | ||||||
| ECTS | 6 | ||||||